Non intrusive load monitoring & identification for energy management system using computational intelligence approach

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dc.contributor.advisorFolly, Komla Aen_ZA
dc.contributor.advisorAwodele, Kehindeen_ZA
dc.contributor.authorAladesanmi, Ereola Johnsonen_ZA
dc.date.accessioned2015-07-29T03:42:03Z
dc.date.available2015-07-29T03:42:03Z
dc.date.issued2015en_ZA
dc.descriptionIncludes bibliography.en_ZA
dc.description.abstractElectrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval.en_ZA
dc.identifier.apacitationAladesanmi, E. J. (2015). <i>Non intrusive load monitoring & identification for energy management system using computational intelligence approach</i>. (Thesis). University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering. Retrieved from http://hdl.handle.net/11427/13561en_ZA
dc.identifier.chicagocitationAladesanmi, Ereola Johnson. <i>"Non intrusive load monitoring & identification for energy management system using computational intelligence approach."</i> Thesis., University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015. http://hdl.handle.net/11427/13561en_ZA
dc.identifier.citationAladesanmi, E. 2015. Non intrusive load monitoring & identification for energy management system using computational intelligence approach. University of Cape Town.en_ZA
dc.identifier.ris TY - Thesis / Dissertation AU - Aladesanmi, Ereola Johnson AB - Electrical energy is the life line to every nation’s or continent development and economic progress. Referable to the recent growth in the demand for electricity and shortage in production, it is indispensable to develop strategies for effective energy management and system delivery. Load monitoring such as intrusive load monitoring, non-intrusive load monitoring, and identification of domestic electrical appliances is proposed especially at the residential level since it is the major energy consumer. The intrusive load monitoring provides accurate results and would allow each individual appliance's energy consumption to be transmitted to a central hub. Nevertheless, there are many practical disadvantages to this method that have motivated the introduction of non-intrusive load monitoring system. The fiscal cost of manufacturing and installing enough monitoring devices to match the number of domestic appliances is considered to be a disadvantage. In addition, the installation of one meter per household appliances would lead to congestion in the house and thus cause inconvenience to the occupants of the house, therefore, non-intrusive load monitoring technique was developed to alleviate the aforementioned challenges of intrusive load monitoring. Non-intrusive load monitoring (NILM) is the process of disaggregating a household’s total energy consumption into its contributing appliances. The total household load is monitored via a single monitoring device such as smart meter (SM). NILM provides cost effective and convenient means of load monitoring and identification. Several nonintrusive load monitoring and identification techniques are reviewed. However, the literature lacks a comprehensive system that can identify appliances with small energy consumption, appliances with overlapping energy consumption and a group of appliance ranges at once. This has been the major setback to most of the adopted techniques. In this dissertation, we propose techniques that overcome these setbacks by combining artificial neural networks (ANN) with a developed algorithm to identify appliances ranges that contribute to the energy consumption within a given period of time usually an hour interval. DA - 2015 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PB - University of Cape Town PY - 2015 T1 - Non intrusive load monitoring & identification for energy management system using computational intelligence approach TI - Non intrusive load monitoring & identification for energy management system using computational intelligence approach UR - http://hdl.handle.net/11427/13561 ER - en_ZA
dc.identifier.urihttp://hdl.handle.net/11427/13561
dc.identifier.vancouvercitationAladesanmi EJ. Non intrusive load monitoring & identification for energy management system using computational intelligence approach. [Thesis]. University of Cape Town ,Faculty of Engineering & the Built Environment ,Department of Electrical Engineering, 2015 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/13561en_ZA
dc.language.isoeng
dc.publisher.departmentDepartment of Electrical Engineeringen_ZA
dc.publisher.facultyFaculty of Engineering and the Built Environment
dc.publisher.institutionUniversity of Cape Town
dc.subject.otherElectrical Engineeringen_ZA
dc.titleNon intrusive load monitoring & identification for energy management system using computational intelligence approachen_ZA
dc.typeMaster Thesis
dc.type.qualificationlevelMasters
dc.type.qualificationnameMSc (Eng)en_ZA
uct.type.filetypeText
uct.type.filetypeImage
uct.type.publicationResearchen_ZA
uct.type.resourceThesisen_ZA
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